Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations

Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations

Joseph Kim, Christian Muise, Ankit Shah, Shubham Agarwal, Julie Shah

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 5591-5598. https://doi.org/10.24963/ijcai.2019/776

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset - i.e., finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL - a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.
Keywords:
Planning and Scheduling: Activity and Plan Recognition
Planning and Scheduling: Search in Planning and Scheduling
Humans and AI: Human-AI Collaboration